Deep learning (DL) shows notable success in biomedical studies. However, most DL algorithms work as black boxes, exclude biomedical experts, and need extensive data. This is especially problematic for fundamental research in the laboratory, where often only small and sparse data are available and the objective is knowledge discovery rather than automation. Furthermore, basic research is usually hypothesis-driven and extensive prior knowledge (priors) exists. To address this, the Self-Enhancing Multi-Photon Artificial Intelligence (SEMPAI) that is designed for multiphoton microscopy (MPM)-based laboratory research is presented. It utilizes meta-learning to optimize prior (and hypothesis) integration, data representation, and neural network architecture simultaneously. By this, the method allows hypothesis testing with DL and provides interpretable feedback about the origin of biological information in 3D images. SEMPAI performs multi-task learning of several related tasks to enable prediction for small datasets. SEMPAI is applied on an extensive MPM database of single muscle fibers from a decade of experiments, resulting in the largest joint analysis of pathologies and function for single muscle fibers to date. It outperforms state-of-the-art biomarkers in six of seven prediction tasks, including those with scarce data. SEMPAI's DL models with integrated priors are superior to those without priors and to prior-only approaches.
Keywords: deep learning; explainable artificial intelligence; meta-learning; multiphoton microscopy; muscle research; prior information integration; scientific machine learning.
© 2023 The Authors. Advanced Science published by Wiley-VCH GmbH.